system

Abstract

We provide the system. [Solution] A means of monitoring users' shopping activities and collecting purchase history data, A means for analyzing the aforementioned purchase history data based on a behavioral economics model to identify the user's unconscious choices, A means for displaying information related to the aforementioned unconscious choice as a pop-up notification on the user's device screen, A system that includes this.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In online shopping, users often make biased selections unconsciously, resulting in duplicate purchases of similar products or impulse purchases of unnecessary items. Such selections are deeply rooted in the user's personal purchasing habits and biases and are difficult to be aware of, thus causing the problem of wasteful spending. There is a need for a system that solves this problem and supports users to make wise and intentional purchasing decisions.

Means for Solving the Problems

[0005] To solve this problem, the present invention provides a means for closely monitoring users' shopping activities and collecting purchase history data. Based on the collected data, analysis is performed using behavioral economics models to identify users' unconscious choices. A means is constructed to display information about the identified unconscious choices as pop-up notifications on the user's device, thereby suggesting to the user that their choices are biased and promoting conscious re-evaluation. Furthermore, since the frequency and content of the pop-up notifications can be adjusted by the user, flexible system operation tailored to individual needs is achieved.

[0006] A "user" is an individual who uses an e-commerce site to shop online, and is the primary target of this system.

[0007] "Shopping activity" refers to the series of actions a user takes on an e-commerce site, from searching for products to browsing and purchasing them. Monitoring this activity is the starting point for the system.

[0008] "Purchase history data" refers to a record of a user's past purchase behavior, including information such as product name, price, purchase date and time, frequency, and category.

[0009] A "behavioral economics model" is a theoretical framework used to analyze the unconscious biases and decision-making processes that users exhibit when selecting products. This model forms the basis for statistically analyzing user purchasing behavior.

[0010] "Unconscious choices" refer to product selections made without the user's awareness, and are influenced by biased tastes, preferences, and purchasing patterns.

[0011] A "pop-up notification" is a short message displayed on a user's device screen, intended to alert them to a specific purchasing action.

[0012] "Bias" refers to inclinations or assumptions that unconsciously influence users' purchasing decisions, leading to specific tendencies in the products they buy and how they choose them.

[0013] A "device" is an electronic device used by a user to access an e-commerce site and make purchases, and includes personal computers, smartphones, and other similar devices. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a processor with a reference number (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, a RAM (Random Access Memory) with a reference number is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a storage with a reference number is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention is a system aimed at reducing unconscious biased choices made by users when shopping on e-commerce sites. The specific operation of the system will be described below.

[0036] First, when a user uses an e-commerce site with their device, the device monitors the user's shopping activity and collects purchase history data. The collected data includes information about the keywords the user searched for, the products they viewed, the products they purchased, and the frequency and timing of those purchases.

[0037] Next, this data is periodically sent from the terminal to the server. Based on the received data, the server analyzes the user's purchasing behavior according to behavioral economics models. This analysis checks whether the user tends to repeatedly make similar choices under certain conditions and identifies unconscious choices.

[0038] If the analysis reveals unconscious choices made by the user, the server generates an appropriate warning message. This message is created using AI generation, with language optimized for the user's purchasing patterns and preferences. For example, a notification might say, "You have purchased similar items multiple times. Do you need them again?"

[0039] The generated notification is sent to the device and displayed as a pop-up on the user's device screen. This pop-up notification is intended to encourage conscious purchasing decisions and prevent future unnecessary purchases.

[0040] Furthermore, users can individually adjust the frequency and content of notifications. This allows for flexible system usage tailored to user preferences and needs, maximizing support for a personalized purchasing experience.

[0041] In this way, the system operates continuously in the background, appropriately supporting the user's purchasing behavior and possessing a practical form for promoting conscious and intelligent purchasing.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The device monitors the user's shopping activity when they use an e-commerce site. During this time, it records keywords the user searches for, product pages viewed, purchased items, and the timing and frequency of these purchases. This forms the basis for collecting user purchase history data.

[0045] Step 2:

[0046] The device periodically sends collected purchase history data to the server. This transmission occurs when the user consents or when the system deems it appropriate. The transmitted data is encrypted and transferred in a privacy-protected manner.

[0047] Step 3:

[0048] The server analyzes the received purchase history data. This analysis applies behavioral economics models to identify users' unconscious choices and purchasing patterns from the data. Specifically, it determines whether users are over-selecting certain product categories or over-responding to discount campaigns.

[0049] Step 4:

[0050] The server generates a pop-up notification to warn the user based on the analysis results. A generation AI is used, and the notification is crafted with the most appropriate wording based on the user's purchasing behavior. For example, it might create a specific notification such as, "You've recently purchased many similar items. Please reconsider whether you need them now."

[0051] Step 5:

[0052] The device receives a pop-up notification sent from the server and displays it on the user's device screen. This notification appears in real time, giving the user an opportunity to pause and reconsider during the purchasing process. The notification is only displayed with the user's consent.

[0053] Step 6:

[0054] When users receive a pop-up notification, they review their purchasing behavior and make a decision. Depending on the content of the notification, they decide whether to cancel the purchase or reconsider and proceed. Users can also individually adjust their notification settings, allowing them to customize the frequency and content of notifications to their needs.

[0055] (Example 1)

[0056] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0057] In online shopping, users make purchasing decisions based on a variety of options, but under certain conditions, they may unconsciously make biased choices. This increases the risk of users purchasing unnecessary items, potentially leading to wasteful spending. Traditional systems do not adequately provide methods to identify such unconscious purchasing behavior and provide appropriate feedback to users.

[0058] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0059] In this invention, the server includes means for monitoring the user's purchasing behavior and collecting behavioral data, means for analyzing the behavioral data and identifying the user's unconscious choices, and means for generating warnings for the identified unconscious choices using a generative AI model. This makes it possible to analyze the user's purchasing behavior, automatically identify trends in unconscious choices, and provide feedback to the user.

[0060] "User" refers to an individual or organization that conducts online shopping through this system.

[0061] "Purchasing behavior" refers to the series of actions a user takes to search for, browse, select, and purchase a product.

[0062] "Behavioral data" refers to information related to a user's purchasing behavior, and includes data such as search keywords, product browsing history, purchase history, and purchase frequency.

[0063] "Unconscious choices" refer to biased product selections made unconsciously by users under specific conditions, and include choices that deviate from normal purchasing decisions.

[0064] A "generative AI model" refers to an artificial intelligence program that generates natural language based on a user's purchasing patterns, and its role is to generate messages optimized for the user.

[0065] A "warning" is a notification given to a user that includes messages intended to inform them of the existence of an unconscious choice and encourage conscious purchasing decisions.

[0066] "Notification" refers to information provided in the form of a warning displayed on the user's device screen, including pop-ups and other visual methods.

[0067] This invention is a system aimed at supporting users' purchasing behavior and reducing unconscious choices when using e-commerce platforms. The specific method for carrying out the invention is described below.

[0068] Users access online shopping sites using standard communication devices. These devices utilize specialized software to collect behavioral data such as user search history, product viewing history, and purchase history, in order to monitor user purchasing behavior. This software runs on the device and is capable of recording data in real time.

[0069] The collected behavioral data is transmitted to the server via a secure communication protocol. The server uses this data for detailed analysis. The analysis employs behavioral economics models and machine learning algorithms to perform calculations that identify whether users are making unconscious purchasing choices in specific patterns. Key technologies used by the server include database management systems and parallel processing techniques.

[0070] Based on the analysis results described above, the server uses a generative AI model to create a warning message for the user. This AI model generates prompts tailored to the user's past purchasing patterns, presenting the warning in an engaging and easy-to-understand manner. An example of a prompt is, "Create a message suggesting a new style to the user based on the fashion items they have purchased in the past."

[0071] The generated warning message is resent to the device and displayed as a pop-up notification on the user's device. This pop-up prompts the user to reconsider their current choice. Not only is this notification encouraged for more conscious purchasing decisions, but users can also freely adjust the notification settings. This provides the system with the flexibility to improve the user's individual purchasing experience.

[0072] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0073] Step 1:

[0074] The terminal uses dedicated software to collect behavioral data in real time when users shop online. Inputs include user search keywords, product browsing history, and purchase history. The terminal organizes this data and prepares it as structured data in a secure format. The output is the collected behavioral data for use in subsequent processing steps.

[0075] Step 2:

[0076] The device sends collected behavioral data to the server at regular intervals. The input is the structured data prepared in step 1. The device encrypts this data and sends it to the server using a secure communication protocol. The output is the behavioral data in a secure format sent to the server.

[0077] Step 3:

[0078] The server analyzes the received behavioral data. The input is behavioral data sent from the terminal. The server applies this data to a behavioral economics model to identify whether the user is unconsciously repeating similar purchasing choices. The output is the analysis results, including the user's unconscious choices.

[0079] Step 4:

[0080] The server prompts the generative AI model based on the analysis results to generate a warning message. The input is the analysis results obtained in step 3. The server utilizes the generative AI model to generate a natural language message optimized for the user's purchasing tendencies. The output is the generated warning message.

[0081] Step 5:

[0082] The server sends a generated warning message to the terminal, which then displays it as a pop-up on the user's device. The input is the warning message sent from the server. The terminal displays the notification in a visually clear format for the user. The output is the pop-up notification displayed to the user.

[0083] (Application Example 1)

[0084] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0085] On e-commerce platforms, users tend to unconsciously make biased purchasing choices, which can result in a lack of diversity in their purchasing behavior. Such bias can impair users' financial efficiency, narrow their product choices, and ultimately lead to decreased customer satisfaction. This invention aims to curb such biased purchasing choices and provide users with a more conscious and diverse purchasing experience.

[0086] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0087] In this invention, the server includes means for monitoring the user's purchasing activities and collecting purchase history data, means for analyzing the purchase history data based on a behavioral economics model to identify the user's unconscious choices, and means for displaying information related to the unconscious choices as a notification on the user's display device. This makes it possible to notify the user of biased purchasing tendencies in real time and suggest products from different categories.

[0088] A "user" is a consumer or individual who purchases goods or services using an e-commerce platform.

[0089] "Purchasing activity" refers to the series of actions a user takes on an e-commerce platform, from searching for products, adding them to their cart, placing an order, and making a purchase.

[0090] "Purchase history data" refers to data about products and services that a user has purchased in the past, including product name, purchase date, price, and quantity.

[0091] Behavioral economics models are statistical and economic analytical methods used to analyze user purchasing behavior and identify unconscious choices and biases.

[0092] "Unconscious choices" are purchasing decisions that users make without realizing it, based on past experiences and habits, and may involve specific patterns or biases.

[0093] A "notification" is an informational message displayed on the user's device screen, and may include feedback on the user's purchasing choices or purchase suggestions.

[0094] A "category" is a classification system based on the type and intended use of a product, serving as a criterion for users to compare and consider their options.

[0095] To implement this invention, the user must first access an e-commerce platform using a device such as a smartphone or smart glasses and begin shopping. The device has a mechanism to continuously collect the user's purchase history data and transmit it to a server at specific intervals. This collected data includes information about the products searched for, what was viewed, what was purchased, and the frequency and duration of these activities.

[0096] Upon receiving this data, the server performs an analysis using behavioral economics models. This analysis includes a process to check whether users are repeatedly making the same choices unconsciously. This reveals potential purchasing biases and inclinations.

[0097] If the server detects a biased purchasing pattern, it uses a generative AI model to generate a warning message for the user. This message is customized based on the user's past purchasing patterns and preferences. A typical message might be something like, "You've purchased multiple similar items. Why not explore a new category?"

[0098] This generated message is immediately sent to the device and displayed as a notification on the user's device screen. The user can use this notification to reconsider before making a purchase.

[0099] For example, if a user consistently purchases clothing from a specific brand, the server can suggest, "Try checking out collections from different designers." An example of a prompt would be, "Create advice suggesting new product categories based on the user's purchase history."

[0100] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0101] Step 1:

[0102] The device collects purchase history data when users engage in shopping activities on e-commerce platforms. Input includes the user's activity history, viewed items, purchased items, and their dates and times. The device aggregates this data and converts it into a data format for periodic transmission to the server. At this stage, the collected data is stored locally.

[0103] Step 2:

[0104] The server receives purchase history data sent from the terminal. This data is used as input and analyzed using a behavioral economics model. The analysis process uses clustering and pattern recognition techniques to check whether users are unconsciously making biased choices. The output generates identified biased choice patterns.

[0105] Step 3:

[0106] If the server detects biased selections as a result of its analysis, it uses a generative AI model to generate a warning message to notify the user. The inputs used are identified biased patterns and information about the user's past purchasing preferences. The output is a customized notification message in language that is easy for the user to understand.

[0107] Step 4:

[0108] The server sends the generated notification message to the device. The device displays the received message as a pop-up notification on the user's device screen. This gives the user an opportunity to reconsider their purchasing behavior.

[0109] Step 5:

[0110] Users can review their purchasing choices through notifications displayed on their devices. In this step, users reconsider whether or not to purchase based on the notification content. As an output, this promotes purchasing behavior in which conscious choices are made.

[0111] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0112] This invention provides more precise and adaptive purchasing support by adding an emotion engine for recognizing user emotions to a system that monitors user shopping activities and collects and analyzes purchase history data. The following describes a specific implementation of this system.

[0113] First, the device monitors the user's shopping activity in real time. During this process, it collects data on the terms the user searches for, the product pages they view, and the items they purchase, and stores this data as purchase history data.

[0114] Next, the emotion engine built into the device recognizes the user's emotions. Emotion recognition can be performed by combining multiple factors, such as the user's facial expression analysis, voice tone, and operation speed. The emotion data obtained here is sent to the server along with the purchase history data.

[0115] The server comprehensively analyzes received purchase history data and emotional data based on behavioral economics models. This analysis not only identifies the user's unconscious choices but also evaluates their emotional state, providing information that supports their purchase intentions and needs.

[0116] Once the analysis is complete, the server generates an optimal pop-up notification based on the user's purchase intent and emotional state. This notification takes the user's current emotions into consideration and is designed to encourage a reassessment of their purchasing behavior. For example, a message tailored to their emotions might be created, such as, "You seem to be feeling a little stressed right now. If you need a change of pace, please reconsider your purchase."

[0117] This notification appears on the device, allowing users to re-evaluate their purchase in light of their own feelings. In addition, users can adjust the content and frequency of notifications, providing flexibility to meet individual needs.

[0118] Ultimately, this system aims to support wiser purchasing decisions by taking into account the dynamic shifts in emotions. This approach allows users to eliminate unconscious biases and achieve a more economically and emotionally satisfying purchasing experience.

[0119] The following describes the processing flow.

[0120] Step 1:

[0121] The device monitors the user's activity on e-commerce sites and collects shopping-related data. Specifically, it obtains data on searched keywords, viewed products, products added to the cart, and purchased products, and stores this as purchase history data.

[0122] Step 2:

[0123] The device's built-in emotion engine recognizes the user's emotional state in real time. Using the user's camera and microphone, it comprehensively evaluates the user's emotions through facial recognition and voice analysis. For example, it analyzes whether the user's face is smiling or their voice sounds tense.

[0124] Step 3:

[0125] The device sends collected purchase history data and sentiment data to the server. Data transmission is secure and encrypted to protect user privacy.

[0126] Step 4:

[0127] The server analyzes the received data based on behavioral economics models to identify the user's unconscious choice patterns. At the same time, it considers emotional data and evaluates how the current emotional state influences purchasing decisions.

[0128] Step 5:

[0129] The server generates the most suitable pop-up notification based on the analysis results. This notification is tailored to the user's emotional state and purchase history, and may include messages such as, "Let's consider if you really need this product. How about taking a different action to change your mood?"

[0130] Step 6:

[0131] The device displays the generated pop-up notification on the user's device screen. Through the displayed notification, the user can re-evaluate their purchasing behavior and make a more informed, emotion-based purchasing decision.

[0132] Step 7:

[0133] After receiving a pop-up notification, users decide whether to proceed with the purchase or reconsider. Furthermore, if the emotion engine's recognition accuracy or the notification content is inappropriate, users can adjust settings and customize the system.

[0134] (Example 2)

[0135] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0136] In users' shopping activities, unconscious choices and temporary emotional states can significantly influence purchasing behavior. This can lead to unnecessary purchases, potentially reducing users' economic and emotional satisfaction. To address this problem, there is a need for systems that enable users to make more informed purchasing decisions.

[0137] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0138] In this invention, the server includes means for monitoring the user's shopping activities and collecting purchase history data; means for acquiring emotional data and recognizing the user's emotional state; means for analyzing the purchase history data and emotional data based on behavioral economics models to identify the user's unconscious choices and emotional state; and means for displaying information related to the unconscious choices and emotional state as a pop-up notification on the user's information display device. This allows the user to recognize whether their choices are influenced by bias and to re-evaluate their purchasing behavior in consideration of their emotional state.

[0139] "User" is a term that refers to an individual who uses a computer or device to engage in shopping activities.

[0140] "Shopping activity" refers to the process by which consumers engage in purchase-related actions, such as searching for, browsing, and purchasing products online or offline.

[0141] "Purchase history data" refers to data that records and organizes information about products a user has searched for, viewed, and purchased in the past.

[0142] "Emotional data" refers to information that reflects the user's emotional state, obtained from factors such as facial expressions, tone of voice, and operation speed.

[0143] A "behavioral economics model" is a theoretical framework for analyzing the psychological and economic factors in users' purchasing behavior and predicting their actions and choices.

[0144] "Unconscious choices" refer to purchasing decisions and actions that users make without being aware of them.

[0145] "Emotional state" refers to the emotional condition or changes a user experiences at a particular moment.

[0146] A "pop-up notification" is an informational message displayed on a user's device screen, used to attract the user's attention and provide feedback on their actions.

[0147] This invention is a system that performs advanced analysis using purchase history data and sentiment data to better support users' purchasing behavior. This system includes a dedicated application installed on a terminal and a server that performs data analysis.

[0148] First, the device monitors the user's shopping activity and accumulates purchase history data in real time. This device is a computer device such as a smartphone or tablet, and has dedicated software installed. This software has the function of collecting data such as the URL of the web page, the time of visit, the product links clicked, and the items added to the cart.

[0149] In addition, the device is equipped with an emotion engine that recognizes the user's emotional state. This recognition is achieved through facial expression analysis using the camera, voice tone analysis using the microphone, and monitoring of operation speed using touch sensors. This allows the system to understand the fluctuations in the user's emotions while they are shopping.

[0150] The collected purchase history and sentiment data are sent to a server for analysis. The server comprehensively analyzes the received data using behavioral economics models to identify users' unconscious choices and emotional states. This analysis is performed particularly by modeling users' purchasing patterns and emotional changes, helping to better understand their intentions and needs regarding purchases.

[0151] Based on the analysis results, the server generates a pop-up notification tailored to the user's emotional state. This notification suggests that the user's choice may be biased and encourages them to re-evaluate their purchasing behavior. For example, a message like, "You seem to be feeling a little stressed right now. If you need a change of pace, please reconsider your purchase," might be possible.

[0152] Ultimately, the device provides these notifications to the user, and the user can adjust the content and frequency of these notifications. Furthermore, it's possible to improve the accuracy and effectiveness of notifications using generative AI models.

[0153] An example of a prompt is, "Analyze the emotional responses a user exhibits while online shopping and generate emotion-based purchasing advice."

[0154] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0155] Step 1:

[0156] The terminal monitors the user's shopping activity and collects purchase history data. Inputs include the user's search queries, visited product pages, and information about purchased items. A dedicated application installed on the terminal collects this data and stores it in a database. The output is detailed purchase history data of the user. This data reveals the user's preferences and interests and is used in the subsequent analysis step.

[0157] Step 2:

[0158] The device activates an emotion engine to recognize the user's emotional state. Inputs include facial expressions captured by the face camera, voice collected by the microphone, and motion data from the touch sensor. These input data are analyzed to obtain an output that evaluates the user's emotions. The analysis is performed using an emotion recognition algorithm to identify emotions such as whether the user is happy or stressed.

[0159] Step 3:

[0160] The device sends purchase history data and sentiment data to the server. The input is the purchase history data and sentiment data that were previously collected and recognized. This data is encrypted and sent to the server over the network. The output is the data that has safely reached the server and is ready to proceed to the next analysis stage.

[0161] Step 4:

[0162] The server analyzes the data it receives. Inputs include purchase history data and sentiment data. The server analyzes this data based on behavioral economics models to evaluate the user's unconscious choices and emotional states. The output is the analysis results, which are used in the next step. A generative AI model is used for the analysis to model the user's choice patterns and emotional influences.

[0163] Step 5:

[0164] The server generates a pop-up notification based on the analysis results. The input is the analyzed data on purchasing behavior and emotional state. Based on this, it generates a message that helps the user recognize that their choices were influenced by emotional bias. The output is a notification message delivered to the user, which encourages them to re-evaluate their purchase.

[0165] Step 6:

[0166] The device receives notifications from the server and displays them to the user. The input is the notification message sent from the server. The device receives this information and displays it on the user's screen. The output is a visually presented notification to the user, which they can then review and use to influence their purchasing behavior. The user can customize the content and frequency of these notifications as output conditions.

[0167] (Application Example 2)

[0168] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0169] Online shoppers often face the challenge of being influenced by unconscious biases and emotions, resulting in undesirable purchases. Furthermore, there is a lack of means to capture the impact of user emotions on purchasing behavior in real time and provide appropriate advice and information.

[0170] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0171] In this invention, the server includes means for monitoring the user's purchasing activities and collecting purchase history data, means for analyzing the purchase history data based on behavioral economics models to identify unintended choices, and means for analyzing emotional states in real time and integrating the analysis of that data. This enables appropriate purchasing support that takes into account the user's emotions.

[0172] A "user" refers to an individual or group that engages in online shopping, and their purchasing activities are monitored by the system.

[0173] "Purchase history data" refers to data that includes information such as terms a user has searched for in the past, product pages they have viewed, and items they have purchased.

[0174] A "behavioral economics model" is an economic theory that takes into account irrational human behavior, and is used to analyze purchasing behavior.

[0175] "Unintentional choices" refer to biased purchasing decisions made by users without their realizing it.

[0176] A "pop-up" is a notification or message that suddenly appears on the user's device screen.

[0177] "Emotional state" refers to the specific circumstances or conditions of a user's emotions, which the system recognizes through facial expressions and voice analysis.

[0178] "Integrated analysis methods" refer to methods that combine and analyze purchase history data and sentiment data, processing them as consistent information.

[0179] "Adjusting purchasing behavior" means that the system intervenes to support appropriate purchasing decisions based on the user's purchasing patterns and emotional state.

[0180] This invention is a system that supports users' purchasing behavior by monitoring their purchasing activities in real time, collecting purchase history data, and analyzing their emotional state. This system mainly consists of terminals and servers.

[0181] The device monitors the user's shopping activity. Specifically, it collects data in real time on the user's smartphone or computer regarding search terms, viewed product pages, and purchased items. This data is stored as purchase history data. Furthermore, the device has an emotion engine that analyzes the user's emotional state using multiple indicators, such as facial expressions, voice tone, and operation speed. The emotion data obtained from this analysis is sent to the server along with the purchase history data.

[0182] The server analyzes received purchase history data and sentiment data based on behavioral economics models. This analysis not only reveals the user's unconscious and potential biases but also evaluates their emotional state, providing information to suppress or encourage purchasing behaviors that users perform unconsciously. The server uses this information to generate appropriate pop-up notifications. These notifications are sensitive to the user's emotions and can, for example, create messages recommending products that match their current emotional state or encouraging them to re-evaluate their purchases.

[0183] The system provides precise and adaptive purchasing support based on emotions and purchase history by displaying pop-up notifications on the user's device screen. Users can adjust the content and frequency of notifications themselves, allowing them to have a purchasing experience tailored to their needs.

[0184] This format allows users to gain a deeper understanding of their own emotions and purchasing behavior, and overcome unconscious biases. Furthermore, the notifications generated by the system support less stressful and more emotionally satisfying purchasing decisions.

[0185] For example, if a user is experiencing everyday stress, the system may display a message such as, "Why not try some relaxation items?" If the user has previously purchased relaxing aromatherapy products, those products may be suggested again.

[0186] An example of a prompt for a generative AI model would be, "Consider the user's stressful situation and provide ideas for application notifications that promote relaxation."

[0187] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0188] Step 1:

[0189] The terminal monitors the user's purchasing activity. Specifically, the terminal collects data in real time regarding the user's search terms, viewed product pages, and purchased items. This data is recorded as purchase history data and used for subsequent processing steps.

[0190] Step 2:

[0191] The device analyzes the user's emotional state using an emotion engine. This involves using the smartphone's camera and microphone to capture the user's facial expressions, voice tone, and operation speed. The emotion engine uses this data as input to perform data processing and calculations to identify the user's emotions, and then outputs emotional data.

[0192] Step 3:

[0193] The terminal sends the collected purchase history data and sentiment data to the server. The input here is the data obtained in steps 1 and 2, and a data packet is generated as the output to be sent to the server.

[0194] Step 4:

[0195] The server analyzes the received purchase history data and sentiment data based on behavioral economics models. The data received in the previous step is used as input, and the data is processed integrally to output information about the user's unconscious choices and current emotional state.

[0196] Step 5:

[0197] The server generates a pop-up notification tailored to the user based on the analysis results. The server considers the target user's emotional state and purchasing tendencies, and uses a generation AI model with prompt text to create an appropriate message. The output is the message text.

[0198] Step 6:

[0199] The server sends the generated pop-up notification to the device. The output is received by the device as a notification and displayed on the user's device screen. Specifically, the user will see a message prompting them to re-evaluate the product before making a purchase.

[0200] Step 7:

[0201] The user re-evaluates the purchase based on the notification. In this step, the user checks the notification on their device and adjusts or postpones their purchase behavior according to the message.

[0202] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0203] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0204] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0205] [Second Embodiment]

[0206] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0207] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0208] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0209] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0210] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0211] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0212] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0213] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0214] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0215] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0216] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0217] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0218] This invention is a system aimed at reducing unconscious biased choices made by users when shopping on e-commerce sites. The specific operation of the system will be described below.

[0219] First, when a user uses an e-commerce site with their device, the device monitors the user's shopping activity and collects purchase history data. The collected data includes information about the keywords the user searched for, the products they viewed, the products they purchased, and the frequency and timing of those purchases.

[0220] Next, this data is periodically sent from the terminal to the server. Based on the received data, the server analyzes the user's purchasing behavior according to behavioral economics models. This analysis checks whether the user tends to repeatedly make similar choices under certain conditions and identifies unconscious choices.

[0221] If the analysis reveals unconscious choices made by the user, the server generates an appropriate warning message. This message is created using AI generation, with language optimized for the user's purchasing patterns and preferences. For example, a notification might say, "You have purchased similar items multiple times. Do you need them again?"

[0222] The generated notification is sent to the device and displayed as a pop-up on the user's device screen. This pop-up notification is intended to encourage conscious purchasing decisions and prevent future unnecessary purchases.

[0223] Furthermore, users can individually adjust the frequency and content of notifications. This allows for flexible system usage tailored to user preferences and needs, maximizing support for a personalized purchasing experience.

[0224] In this way, the system operates continuously in the background, appropriately supporting the user's purchasing behavior and possessing a practical form for promoting conscious and intelligent purchasing.

[0225] The following describes the processing flow.

[0226] Step 1:

[0227] The device monitors the user's shopping activity when they use an e-commerce site. During this time, it records keywords the user searches for, product pages viewed, purchased items, and the timing and frequency of these purchases. This forms the basis for collecting user purchase history data.

[0228] Step 2:

[0229] The device periodically sends collected purchase history data to the server. This transmission occurs when the user consents or when the system deems it appropriate. The transmitted data is encrypted and transferred in a privacy-protected manner.

[0230] Step 3:

[0231] The server analyzes the received purchase history data. This analysis applies behavioral economics models to identify users' unconscious choices and purchasing patterns from the data. Specifically, it determines whether users are over-selecting certain product categories or over-responding to discount campaigns.

[0232] Step 4:

[0233] The server generates a pop-up notification to warn the user based on the analysis results. A generation AI is used, and the notification is crafted with the most appropriate wording based on the user's purchasing behavior. For example, it might create a specific notification such as, "You've recently purchased many similar items. Please reconsider whether you need them now."

[0234] Step 5:

[0235] The device receives a pop-up notification sent from the server and displays it on the user's device screen. This notification appears in real time, giving the user an opportunity to pause and reconsider during the purchasing process. The notification is only displayed with the user's consent.

[0236] Step 6:

[0237] When users receive a pop-up notification, they review their purchasing behavior and make a decision. Depending on the content of the notification, they decide whether to cancel the purchase or reconsider and proceed. Users can also individually adjust their notification settings, allowing them to customize the frequency and content of notifications to their needs.

[0238] (Example 1)

[0239] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0240] In online shopping, users make purchasing decisions based on a variety of options, but under certain conditions, they may unconsciously make biased choices. This increases the risk of users purchasing unnecessary items, potentially leading to wasteful spending. Traditional systems do not adequately provide methods to identify such unconscious purchasing behavior and provide appropriate feedback to users.

[0241] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0242] In this invention, the server includes means for monitoring the user's purchasing behavior and collecting behavioral data, means for analyzing the behavioral data and identifying the user's unconscious choices, and means for generating warnings for the identified unconscious choices using a generative AI model. This makes it possible to analyze the user's purchasing behavior, automatically identify trends in unconscious choices, and provide feedback to the user.

[0243] "User" refers to an individual or organization that conducts online shopping through this system.

[0244] "Purchasing behavior" refers to the series of actions a user takes to search for, browse, select, and purchase a product.

[0245] "Behavioral data" refers to information related to a user's purchasing behavior, and includes data such as search keywords, product browsing history, purchase history, and purchase frequency.

[0246] "Unconscious choices" refer to biased product selections made unconsciously by users under specific conditions, and include choices that deviate from normal purchasing decisions.

[0247] A "generative AI model" refers to an artificial intelligence program that generates natural language based on a user's purchasing patterns, and its role is to generate messages optimized for the user.

[0248] A "warning" is a notification given to a user that includes messages intended to inform them of the existence of an unconscious choice and encourage conscious purchasing decisions.

[0249] "Notification" refers to information provided in the form of a warning displayed on the user's device screen, including pop-ups and other visual methods.

[0250] This invention is a system aimed at supporting users' purchasing behavior and reducing unconscious choices when using e-commerce platforms. The specific method for carrying out the invention is described below.

[0251] Users access online shopping sites using standard communication devices. These devices utilize specialized software to collect behavioral data such as user search history, product viewing history, and purchase history, in order to monitor user purchasing behavior. This software runs on the device and is capable of recording data in real time.

[0252] The collected behavioral data is transmitted to the server via a secure communication protocol. The server uses this data for detailed analysis. The analysis employs behavioral economics models and machine learning algorithms to perform calculations that identify whether users are making unconscious purchasing choices in specific patterns. Key technologies used by the server include database management systems and parallel processing techniques.

[0253] Based on the analysis results described above, the server uses a generative AI model to create a warning message for the user. This AI model generates prompts tailored to the user's past purchasing patterns, presenting the warning in an engaging and easy-to-understand manner. An example of a prompt is, "Create a message suggesting a new style to the user based on the fashion items they have purchased in the past."

[0254] The generated warning message is resent to the device and displayed as a pop-up notification on the user's device. This pop-up prompts the user to reconsider their current choice. Not only is this notification encouraged for more conscious purchasing decisions, but users can also freely adjust the notification settings. This provides the system with the flexibility to improve the user's individual purchasing experience.

[0255] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0256] Step 1:

[0257] The terminal uses dedicated software to collect behavioral data in real time when users shop online. Inputs include user search keywords, product browsing history, and purchase history. The terminal organizes this data and prepares it as structured data in a secure format. The output is the collected behavioral data for use in subsequent processing steps.

[0258] Step 2:

[0259] The device sends collected behavioral data to the server at regular intervals. The input is the structured data prepared in step 1. The device encrypts this data and sends it to the server using a secure communication protocol. The output is the behavioral data in a secure format sent to the server.

[0260] Step 3:

[0261] The server analyzes the received behavioral data. The input is behavioral data sent from the terminal. The server applies this data to a behavioral economics model to identify whether the user is unconsciously repeating similar purchasing choices. The output is the analysis results, including the user's unconscious choices.

[0262] Step 4:

[0263] The server prompts the generative AI model based on the analysis results to generate a warning message. The input is the analysis results obtained in step 3. The server utilizes the generative AI model to generate a natural language message optimized for the user's purchasing tendencies. The output is the generated warning message.

[0264] Step 5:

[0265] The server sends a generated warning message to the terminal, which then displays it as a pop-up on the user's device. The input is the warning message sent from the server. The terminal displays the notification in a visually clear format for the user. The output is the pop-up notification displayed to the user.

[0266] (Application Example 1)

[0267] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0268] On e-commerce platforms, users tend to unconsciously make biased purchasing choices, which can result in a lack of diversity in their purchasing behavior. Such bias can impair users' financial efficiency, narrow their product choices, and ultimately lead to decreased customer satisfaction. This invention aims to curb such biased purchasing choices and provide users with a more conscious and diverse purchasing experience.

[0269] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0270] In this invention, the server includes means for monitoring the user's purchasing activities and collecting purchase history data, means for analyzing the purchase history data based on a behavioral economics model to identify the user's unconscious choices, and means for displaying information related to the unconscious choices as a notification on the user's display device. This makes it possible to notify the user of biased purchasing tendencies in real time and suggest products from different categories.

[0271] A "user" is a consumer or individual who purchases goods or services using an e-commerce platform.

[0272] "Purchasing activity" refers to the series of actions a user takes on an e-commerce platform, from searching for products, adding them to their cart, placing an order, and making a purchase.

[0273] "Purchase history data" refers to data about products and services that a user has purchased in the past, including product name, purchase date, price, and quantity.

[0274] Behavioral economics models are statistical and economic analytical methods used to analyze user purchasing behavior and identify unconscious choices and biases.

[0275] "Unconscious choices" are purchasing decisions that users make without realizing it, based on past experiences and habits, and may involve specific patterns or biases.

[0276] A "notification" is an informational message displayed on the user's device screen, and may include feedback on the user's purchasing choices or purchase suggestions.

[0277] A "category" is a classification system based on the type and intended use of a product, serving as a criterion for users to compare and consider their options.

[0278] To implement this invention, the user must first access an e-commerce platform using a device such as a smartphone or smart glasses and begin shopping. The device has a mechanism to continuously collect the user's purchase history data and transmit it to a server at specific intervals. This collected data includes information about the products searched for, what was viewed, what was purchased, and the frequency and duration of these activities.

[0279] Upon receiving this data, the server performs an analysis using behavioral economics models. This analysis includes a process to check whether users are repeatedly making the same choices unconsciously. This reveals potential purchasing biases and inclinations.

[0280] If the server detects a biased purchasing pattern, it uses a generative AI model to generate a warning message for the user. This message is customized based on the user's past purchasing patterns and preferences. A typical message might be something like, "You've purchased multiple similar items. Why not explore a new category?"

[0281] This generated message is immediately sent to the device and displayed as a notification on the user's device screen. The user can use this notification to reconsider before making a purchase.

[0282] As a specific example, when a user only purchases clothing items of a specific brand, the server can make a proposal such as "Please check out the collections of different designers." An example of a prompt sentence is "Based on the user's purchase history, please create advice for proposing products in a new category."

[0283] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0284] Step 1:

[0285] When the user conducts shopping activities on the e-commerce platform, the terminal collects purchase history data. The input includes the user's operation history, viewed products, purchased products, and their dates and times. The terminal summarizes these data and converts them into a data format for regular transmission to the server. At this stage, the collected data is stored locally.

[0286] Step 2:

[0287] The server receives the purchase history data transmitted from the terminal. Using this data as input, it performs analysis using behavioral economics models. In the analysis process, clustering and pattern recognition techniques are used to check whether the user is making unconsciously biased selections. As output, a specific biased selection pattern is generated.

[0288] Step 3:

[0289] If a biased selection is recognized as a result of the analysis, the server uses the generated AI model to generate a warning message for notifying the user. As input, the specific biased pattern and information regarding the user's past purchase preferences are used. The output is a customized notification message in words that are easy for the user to understand.

[0290] Step 4:

[0291] The server sends the generated notification message to the device. The device displays the received message as a pop-up notification on the user's device screen. This gives the user an opportunity to reconsider their purchasing behavior.

[0292] Step 5:

[0293] Users can review their purchasing choices through notifications displayed on their devices. In this step, users reconsider whether or not to purchase based on the notification content. As an output, this promotes purchasing behavior in which conscious choices are made.

[0294] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0295] This invention provides more precise and adaptive purchasing support by adding an emotion engine for recognizing user emotions to a system that monitors user shopping activities and collects and analyzes purchase history data. The following describes a specific implementation of this system.

[0296] First, the device monitors the user's shopping activity in real time. During this process, it collects data on the terms the user searches for, the product pages they view, and the items they purchase, and stores this data as purchase history data.

[0297] Next, the emotion engine built into the device recognizes the user's emotions. Emotion recognition can be performed by combining multiple factors, such as the user's facial expression analysis, voice tone, and operation speed. The emotion data obtained here is sent to the server along with the purchase history data.

[0298] The server comprehensively analyzes received purchase history data and emotional data based on behavioral economics models. This analysis not only identifies the user's unconscious choices but also evaluates their emotional state, providing information that supports their purchase intentions and needs.

[0299] Once the analysis is complete, the server generates an optimal pop-up notification based on the user's purchase intent and emotional state. This notification takes the user's current emotions into consideration and is designed to encourage a reassessment of their purchasing behavior. For example, a message tailored to their emotions might be created, such as, "You seem to be feeling a little stressed right now. If you need a change of pace, please reconsider your purchase."

[0300] This notification appears on the device, allowing users to re-evaluate their purchase in light of their own feelings. In addition, users can adjust the content and frequency of notifications, providing flexibility to meet individual needs.

[0301] Ultimately, this system aims to support wiser purchasing decisions by taking into account the dynamic shifts in emotions. This approach allows users to eliminate unconscious biases and achieve a more economically and emotionally satisfying purchasing experience.

[0302] The following describes the processing flow.

[0303] Step 1:

[0304] The device monitors the user's activity on e-commerce sites and collects shopping-related data. Specifically, it obtains data on searched keywords, viewed products, products added to the cart, and purchased products, and stores this as purchase history data.

[0305] Step 2:

[0306] The emotion engine built into the terminal recognizes the user's emotional state in real time. Here, the user's camera and microphone are used to comprehensively evaluate the user's emotions through facial expression recognition and voice analysis. For example, it analyzes whether the user's expression is smiling or whether the voice is tense.

[0307] Step 3:

[0308] The terminal sends the collected purchase history data and emotion data to the server. The data transmission is carried out securely after encryption, taking into account the user's privacy.

[0309] Step 4:

[0310] The server analyzes the received data based on the behavioral economics model to identify the user's unconscious choice patterns. At the same time, considering the emotion data, it evaluates how the current emotional state affects the purchase.

[0311] Step 5:

[0312] The server generates an optimal pop-up notification based on the analysis results. This notification is tailored to the user's emotional state and purchase history, and includes messages such as "Let's confirm if you really need this product. How about taking a different action to cheer yourself up?"

[0313] Step 6:

[0314] The terminal displays the generated pop-up notification on the user's device screen. Through the displayed notification, the user can re-evaluate their purchase behavior and make a wise purchase decision based on their emotions.

[0315] Step 7:

[0316] After receiving a pop-up notification, users decide whether to proceed with the purchase or reconsider. Furthermore, if the emotion engine's recognition accuracy or the notification content is inappropriate, users can adjust settings and customize the system.

[0317] (Example 2)

[0318] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0319] In users' shopping activities, unconscious choices and temporary emotional states can significantly influence purchasing behavior. This can lead to unnecessary purchases, potentially reducing users' economic and emotional satisfaction. To address this problem, there is a need for systems that enable users to make more informed purchasing decisions.

[0320] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0321] In this invention, the server includes means for monitoring the user's shopping activities and collecting purchase history data; means for acquiring emotional data and recognizing the user's emotional state; means for analyzing the purchase history data and emotional data based on behavioral economics models to identify the user's unconscious choices and emotional state; and means for displaying information related to the unconscious choices and emotional state as a pop-up notification on the user's information display device. This allows the user to recognize whether their choices are influenced by bias and to re-evaluate their purchasing behavior in consideration of their emotional state.

[0322] "User" is a term that refers to an individual who uses a computer or device to engage in shopping activities.

[0323] "Shopping activity" refers to the process by which consumers engage in purchase-related actions, such as searching for, browsing, and purchasing products online or offline.

[0324] "Purchase history data" refers to data that records and organizes information about products a user has searched for, viewed, and purchased in the past.

[0325] "Emotional data" refers to information that reflects the user's emotional state, obtained from factors such as facial expressions, tone of voice, and operation speed.

[0326] A "behavioral economics model" is a theoretical framework for analyzing the psychological and economic factors in users' purchasing behavior and predicting their actions and choices.

[0327] "Unconscious choices" refer to purchasing decisions and actions that users make without being aware of them.

[0328] "Emotional state" refers to the emotional condition or changes a user experiences at a particular moment.

[0329] A "pop-up notification" is an informational message displayed on a user's device screen, used to attract the user's attention and provide feedback on their actions.

[0330] This invention is a system that performs advanced analysis using purchase history data and sentiment data to better support users' purchasing behavior. This system includes a dedicated application installed on a terminal and a server that performs data analysis.

[0331] First, the device monitors the user's shopping activity and accumulates purchase history data in real time. This device is a computer device such as a smartphone or tablet, and has dedicated software installed. This software has the function of collecting data such as the URL of the web page, the time of visit, the product links clicked, and the items added to the cart.

[0332] In addition, the device is equipped with an emotion engine that recognizes the user's emotional state. This recognition is achieved through facial expression analysis using the camera, voice tone analysis using the microphone, and monitoring of operation speed using touch sensors. This allows the system to understand the fluctuations in the user's emotions while they are shopping.

[0333] The collected purchase history and sentiment data are sent to a server for analysis. The server comprehensively analyzes the received data using behavioral economics models to identify users' unconscious choices and emotional states. This analysis is performed particularly by modeling users' purchasing patterns and emotional changes, helping to better understand their intentions and needs regarding purchases.

[0334] Based on the analysis results, the server generates a pop-up notification tailored to the user's emotional state. This notification suggests that the user's choice may be biased and encourages them to re-evaluate their purchasing behavior. For example, a message like, "You seem to be feeling a little stressed right now. If you need a change of pace, please reconsider your purchase," might be possible.

[0335] Ultimately, the device provides these notifications to the user, and the user can adjust the content and frequency of these notifications. Furthermore, it's possible to improve the accuracy and effectiveness of notifications using generative AI models.

[0336] An example of a prompt is, "Analyze the emotional responses a user exhibits while online shopping and generate emotion-based purchasing advice."

[0337] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0338] Step 1:

[0339] The terminal monitors the user's shopping activity and collects purchase history data. Inputs include the user's search queries, visited product pages, and information about purchased items. A dedicated application installed on the terminal collects this data and stores it in a database. The output is detailed purchase history data of the user. This data reveals the user's preferences and interests and is used in the subsequent analysis step.

[0340] Step 2:

[0341] The device activates an emotion engine to recognize the user's emotional state. Inputs include facial expressions captured by the face camera, voice collected by the microphone, and motion data from the touch sensor. These input data are analyzed to obtain an output that evaluates the user's emotions. The analysis is performed using an emotion recognition algorithm to identify emotions such as whether the user is happy or stressed.

[0342] Step 3:

[0343] The device sends purchase history data and sentiment data to the server. The input is the purchase history data and sentiment data that were previously collected and recognized. This data is encrypted and sent to the server over the network. The output is the data that has safely reached the server and is ready to proceed to the next analysis stage.

[0344] Step 4:

[0345] The server analyzes the data it receives. Inputs include purchase history data and sentiment data. The server analyzes this data based on behavioral economics models to evaluate the user's unconscious choices and emotional states. The output is the analysis results, which are used in the next step. A generative AI model is used for the analysis to model the user's choice patterns and emotional influences.

[0346] Step 5:

[0347] The server generates a pop-up notification based on the analysis results. The input is the analyzed data on purchasing behavior and emotional state. Based on this, it generates a message that helps the user recognize that their choices were influenced by emotional bias. The output is a notification message delivered to the user, which encourages them to re-evaluate their purchase.

[0348] Step 6:

[0349] The device receives notifications from the server and displays them to the user. The input is the notification message sent from the server. The device receives this information and displays it on the user's screen. The output is a visually presented notification to the user, which they can then review and use to influence their purchasing behavior. The user can customize the content and frequency of these notifications as output conditions.

[0350] (Application Example 2)

[0351] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0352] Online shoppers often face the challenge of being influenced by unconscious biases and emotions, resulting in undesirable purchases. Furthermore, there is a lack of means to capture the impact of user emotions on purchasing behavior in real time and provide appropriate advice and information.

[0353] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0354] In this invention, the server includes means for monitoring the user's purchasing activities and collecting purchase history data, means for analyzing the purchase history data based on behavioral economics models to identify unintended choices, and means for analyzing emotional states in real time and integrating the analysis of that data. This enables appropriate purchasing support that takes into account the user's emotions.

[0355] A "user" refers to an individual or group that engages in online shopping, and their purchasing activities are monitored by the system.

[0356] "Purchase history data" refers to data that includes information such as terms a user has searched for in the past, product pages they have viewed, and items they have purchased.

[0357] A "behavioral economics model" is an economic theory that takes into account irrational human behavior, and is used to analyze purchasing behavior.

[0358] "Unintentional choices" refer to biased purchasing decisions made by users without their realizing it.

[0359] A "pop-up" is a notification or message that suddenly appears on the user's device screen.

[0360] "Emotional state" refers to the specific circumstances or conditions of a user's emotions, which the system recognizes through facial expressions and voice analysis.

[0361] "Integrated analysis methods" refer to methods that combine and analyze purchase history data and sentiment data, processing them as consistent information.

[0362] "Adjusting purchasing behavior" means that the system intervenes to support appropriate purchasing decisions based on the user's purchasing patterns and emotional state.

[0363] This invention is a system that supports users' purchasing behavior by monitoring their purchasing activities in real time, collecting purchase history data, and analyzing their emotional state. This system mainly consists of terminals and servers.

[0364] The device monitors the user's shopping activity. Specifically, it collects data in real time on the user's smartphone or computer regarding search terms, viewed product pages, and purchased items. This data is stored as purchase history data. Furthermore, the device has an emotion engine that analyzes the user's emotional state using multiple indicators, such as facial expressions, voice tone, and operation speed. The emotion data obtained from this analysis is sent to the server along with the purchase history data.

[0365] The server analyzes received purchase history data and sentiment data based on behavioral economics models. This analysis not only reveals the user's unconscious and potential biases but also evaluates their emotional state, providing information to suppress or encourage purchasing behaviors that users perform unconsciously. The server uses this information to generate appropriate pop-up notifications. These notifications are sensitive to the user's emotions and can, for example, create messages recommending products that match their current emotional state or encouraging them to re-evaluate their purchases.

[0366] The system provides precise and adaptive purchasing support based on emotions and purchase history by displaying pop-up notifications on the user's device screen. Users can adjust the content and frequency of notifications themselves, allowing them to have a purchasing experience tailored to their needs.

[0367] This format allows users to gain a deeper understanding of their own emotions and purchasing behavior, and overcome unconscious biases. Furthermore, the notifications generated by the system support less stressful and more emotionally satisfying purchasing decisions.

[0368] For example, if a user is experiencing everyday stress, the system may display a message such as, "Why not try some relaxation items?" If the user has previously purchased relaxing aromatherapy products, those products may be suggested again.

[0369] An example of a prompt for a generative AI model would be, "Consider the user's stressful situation and provide ideas for application notifications that promote relaxation."

[0370] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0371] Step 1:

[0372] The terminal monitors the user's purchasing activity. Specifically, the terminal collects data in real time regarding the user's search terms, viewed product pages, and purchased items. This data is recorded as purchase history data and used for subsequent processing steps.

[0373] Step 2:

[0374] The device analyzes the user's emotional state using an emotion engine. This involves using the smartphone's camera and microphone to capture the user's facial expressions, voice tone, and operation speed. The emotion engine uses this data as input to perform data processing and calculations to identify the user's emotions, and then outputs emotional data.

[0375] Step 3:

[0376] The terminal sends the collected purchase history data and sentiment data to the server. The input here is the data obtained in steps 1 and 2, and a data packet is generated as the output to be sent to the server.

[0377] Step 4:

[0378] The server analyzes the received purchase history data and sentiment data based on behavioral economics models. The data received in the previous step is used as input, and the data is processed integrally to output information about the user's unconscious choices and current emotional state.

[0379] Step 5:

[0380] The server generates a pop-up notification tailored to the user based on the analysis results. The server considers the target user's emotional state and purchasing tendencies, and uses a generation AI model with prompt text to create an appropriate message. The output is the message text.

[0381] Step 6:

[0382] The server sends the generated pop-up notification to the device. The output is received by the device as a notification and displayed on the user's device screen. Specifically, the user will see a message prompting them to re-evaluate the product before making a purchase.

[0383] Step 7:

[0384] The user re-evaluates the purchase based on the notification. In this step, the user checks the notification on their device and adjusts or postpones their purchase behavior according to the message.

[0385] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0386] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0387] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0388] [Third Embodiment]

[0389] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0390] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0391] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0392] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0393] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0394] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0395] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0396] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0397] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0398] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0399] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0400] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0401] This invention is a system aimed at reducing unconscious biased choices made by users when shopping on e-commerce sites. The specific operation of the system will be described below.

[0402] First, when a user uses an e-commerce site with their device, the device monitors the user's shopping activity and collects purchase history data. The collected data includes information about the keywords the user searched for, the products they viewed, the products they purchased, and the frequency and timing of those purchases.

[0403] Next, this data is periodically sent from the terminal to the server. Based on the received data, the server analyzes the user's purchasing behavior according to behavioral economics models. This analysis checks whether the user tends to repeatedly make similar choices under certain conditions and identifies unconscious choices.

[0404] If the analysis reveals unconscious choices made by the user, the server generates an appropriate warning message. This message is created using AI generation, with language optimized for the user's purchasing patterns and preferences. For example, a notification might say, "You have purchased similar items multiple times. Do you need them again?"

[0405] The generated notification is sent to the device and displayed as a pop-up on the user's device screen. This pop-up notification is intended to encourage conscious purchasing decisions and prevent future unnecessary purchases.

[0406] Furthermore, users can individually adjust the frequency and content of notifications. This allows for flexible system usage tailored to user preferences and needs, maximizing support for a personalized purchasing experience.

[0407] In this way, the system operates continuously in the background, appropriately supporting the user's purchasing behavior and possessing a practical form for promoting conscious and intelligent purchasing.

[0408] The following describes the processing flow.

[0409] Step 1:

[0410] The device monitors the user's shopping activity when they use an e-commerce site. During this time, it records keywords the user searches for, product pages viewed, purchased items, and the timing and frequency of these purchases. This forms the basis for collecting user purchase history data.

[0411] Step 2:

[0412] The device periodically sends collected purchase history data to the server. This transmission occurs when the user consents or when the system deems it appropriate. The transmitted data is encrypted and transferred in a privacy-protected manner.

[0413] Step 3:

[0414] The server analyzes the received purchase history data. This analysis applies behavioral economics models to identify users' unconscious choices and purchasing patterns from the data. Specifically, it determines whether users are over-selecting certain product categories or over-responding to discount campaigns.

[0415] Step 4:

[0416] The server generates a pop-up notification to warn the user based on the analysis results. A generation AI is used, and the notification is crafted with the most appropriate wording based on the user's purchasing behavior. For example, it might create a specific notification such as, "You've recently purchased many similar items. Please reconsider whether you need them now."

[0417] Step 5:

[0418] The device receives a pop-up notification sent from the server and displays it on the user's device screen. This notification appears in real time, giving the user an opportunity to pause and reconsider during the purchasing process. The notification is only displayed with the user's consent.

[0419] Step 6:

[0420] When users receive a pop-up notification, they review their purchasing behavior and make a decision. Depending on the content of the notification, they decide whether to cancel the purchase or reconsider and proceed. Users can also individually adjust their notification settings, allowing them to customize the frequency and content of notifications to their needs.

[0421] (Example 1)

[0422] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0423] In online shopping, users make purchasing decisions based on a variety of options, but under certain conditions, they may unconsciously make biased choices. This increases the risk of users purchasing unnecessary items, potentially leading to wasteful spending. Traditional systems do not adequately provide methods to identify such unconscious purchasing behavior and provide appropriate feedback to users.

[0424] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0425] In this invention, the server includes means for monitoring the user's purchasing behavior and collecting behavioral data, means for analyzing the behavioral data and identifying the user's unconscious choices, and means for generating warnings for the identified unconscious choices using a generative AI model. This makes it possible to analyze the user's purchasing behavior, automatically identify trends in unconscious choices, and provide feedback to the user.

[0426] "User" refers to an individual or organization that conducts online shopping through this system.

[0427] "Purchasing behavior" refers to the series of actions a user takes to search for, browse, select, and purchase a product.

[0428] "Behavioral data" refers to information related to a user's purchasing behavior, and includes data such as search keywords, product browsing history, purchase history, and purchase frequency.

[0429] "Unconscious choices" refer to biased product selections made unconsciously by users under specific conditions, and include choices that deviate from normal purchasing decisions.

[0430] A "generative AI model" refers to an artificial intelligence program that generates natural language based on a user's purchasing patterns, and its role is to generate messages optimized for the user.

[0431] A "warning" is a notification given to a user that includes messages intended to inform them of the existence of an unconscious choice and encourage conscious purchasing decisions.

[0432] "Notification" refers to information provided in the form of a warning displayed on the user's device screen, including pop-ups and other visual methods.

[0433] This invention is a system aimed at supporting users' purchasing behavior and reducing unconscious choices when using e-commerce platforms. The specific method for carrying out the invention is described below.

[0434] Users access online shopping sites using standard communication devices. These devices utilize specialized software to collect behavioral data such as user search history, product viewing history, and purchase history, in order to monitor user purchasing behavior. This software runs on the device and is capable of recording data in real time.

[0435] The collected behavioral data is transmitted to the server via a secure communication protocol. The server uses this data for detailed analysis. The analysis employs behavioral economics models and machine learning algorithms to perform calculations that identify whether users are making unconscious purchasing choices in specific patterns. Key technologies used by the server include database management systems and parallel processing techniques.

[0436] Based on the analysis results described above, the server uses a generative AI model to create a warning message for the user. This AI model generates prompts tailored to the user's past purchasing patterns, presenting the warning in an engaging and easy-to-understand manner. An example of a prompt is, "Create a message suggesting a new style to the user based on the fashion items they have purchased in the past."

[0437] The generated warning message is resent to the device and displayed as a pop-up notification on the user's device. This pop-up prompts the user to reconsider their current choice. Not only is this notification encouraged for more conscious purchasing decisions, but users can also freely adjust the notification settings. This provides the system with the flexibility to improve the user's individual purchasing experience.

[0438] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0439] Step 1:

[0440] The terminal uses dedicated software to collect behavioral data in real time when users shop online. Inputs include user search keywords, product browsing history, and purchase history. The terminal organizes this data and prepares it as structured data in a secure format. The output is the collected behavioral data for use in subsequent processing steps.

[0441] Step 2:

[0442] The device sends collected behavioral data to the server at regular intervals. The input is the structured data prepared in step 1. The device encrypts this data and sends it to the server using a secure communication protocol. The output is the behavioral data in a secure format sent to the server.

[0443] Step 3:

[0444] The server analyzes the received behavioral data. The input is behavioral data sent from the terminal. The server applies this data to a behavioral economics model to identify whether the user is unconsciously repeating similar purchasing choices. The output is the analysis results, including the user's unconscious choices.

[0445] Step 4:

[0446] The server prompts the generative AI model based on the analysis results to generate a warning message. The input is the analysis results obtained in step 3. The server utilizes the generative AI model to generate a natural language message optimized for the user's purchasing tendencies. The output is the generated warning message.

[0447] Step 5:

[0448] The server sends a generated warning message to the terminal, which then displays it as a pop-up on the user's device. The input is the warning message sent from the server. The terminal displays the notification in a visually clear format for the user. The output is the pop-up notification displayed to the user.

[0449] (Application Example 1)

[0450] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0451] On e-commerce platforms, users tend to unconsciously make biased purchasing choices, which can result in a lack of diversity in their purchasing behavior. Such bias can impair users' financial efficiency, narrow their product choices, and ultimately lead to decreased customer satisfaction. This invention aims to curb such biased purchasing choices and provide users with a more conscious and diverse purchasing experience.

[0452] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0453] In this invention, the server includes means for monitoring the user's purchasing activities and collecting purchase history data, means for analyzing the purchase history data based on a behavioral economics model to identify the user's unconscious choices, and means for displaying information related to the unconscious choices as a notification on the user's display device. This makes it possible to notify the user of biased purchasing tendencies in real time and suggest products from different categories.

[0454] A "user" is a consumer or individual who purchases goods or services using an e-commerce platform.

[0455] "Purchasing activity" refers to the series of actions a user takes on an e-commerce platform, from searching for products, adding them to their cart, placing an order, and making a purchase.

[0456] "Purchase history data" refers to data about products and services that a user has purchased in the past, including product name, purchase date, price, and quantity.

[0457] Behavioral economics models are statistical and economic analytical methods used to analyze user purchasing behavior and identify unconscious choices and biases.

[0458] "Unconscious choices" are purchasing decisions that users make without realizing it, based on past experiences and habits, and may involve specific patterns or biases.

[0459] A "notification" is an informational message displayed on the user's device screen, and may include feedback on the user's purchasing choices or purchase suggestions.

[0460] A "category" is a classification system based on the type and intended use of a product, serving as a criterion for users to compare and consider their options.

[0461] To implement this invention, the user must first access an e-commerce platform using a device such as a smartphone or smart glasses and begin shopping. The device has a mechanism to continuously collect the user's purchase history data and transmit it to a server at specific intervals. This collected data includes information about the products searched for, what was viewed, what was purchased, and the frequency and duration of these activities.

[0462] Upon receiving this data, the server performs an analysis using behavioral economics models. This analysis includes a process to check whether users are repeatedly making the same choices unconsciously. This reveals potential purchasing biases and inclinations.

[0463] If the server detects a biased purchasing pattern, it uses a generative AI model to generate a warning message for the user. This message is customized based on the user's past purchasing patterns and preferences. A typical message might be something like, "You've purchased multiple similar items. Why not explore a new category?"

[0464] This generated message is immediately sent to the device and displayed as a notification on the user's device screen. The user can use this notification to reconsider before making a purchase.

[0465] For example, if a user consistently purchases clothing from a specific brand, the server can suggest, "Try checking out collections from different designers." An example of a prompt would be, "Create advice suggesting new product categories based on the user's purchase history."

[0466] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0467] Step 1:

[0468] The device collects purchase history data when users engage in shopping activities on e-commerce platforms. Input includes the user's activity history, viewed items, purchased items, and their dates and times. The device aggregates this data and converts it into a data format for periodic transmission to the server. At this stage, the collected data is stored locally.

[0469] Step 2:

[0470] The server receives purchase history data sent from the terminal. This data is used as input and analyzed using a behavioral economics model. The analysis process uses clustering and pattern recognition techniques to check whether users are unconsciously making biased choices. The output generates identified biased choice patterns.

[0471] Step 3:

[0472] If the server detects biased selections as a result of its analysis, it uses a generative AI model to generate a warning message to notify the user. The inputs used are identified biased patterns and information about the user's past purchasing preferences. The output is a customized notification message in language that is easy for the user to understand.

[0473] Step 4:

[0474] The server sends the generated notification message to the device. The device displays the received message as a pop-up notification on the user's device screen. This gives the user an opportunity to reconsider their purchasing behavior.

[0475] Step 5:

[0476] Users can review their purchasing choices through notifications displayed on their devices. In this step, users reconsider whether or not to purchase based on the notification content. As an output, this promotes purchasing behavior in which conscious choices are made.

[0477] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0478] This invention provides more precise and adaptive purchasing support by adding an emotion engine for recognizing user emotions to a system that monitors user shopping activities and collects and analyzes purchase history data. The following describes a specific implementation of this system.

[0479] First, the device monitors the user's shopping activity in real time. During this process, it collects data on the terms the user searches for, the product pages they view, and the items they purchase, and stores this data as purchase history data.

[0480] Next, the emotion engine built into the device recognizes the user's emotions. Emotion recognition can be performed by combining multiple factors, such as the user's facial expression analysis, voice tone, and operation speed. The emotion data obtained here is sent to the server along with the purchase history data.

[0481] The server comprehensively analyzes received purchase history data and emotional data based on behavioral economics models. This analysis not only identifies the user's unconscious choices but also evaluates their emotional state, providing information that supports their purchase intentions and needs.

[0482] Once the analysis is complete, the server generates an optimal pop-up notification based on the user's purchase intent and emotional state. This notification takes the user's current emotions into consideration and is designed to encourage a reassessment of their purchasing behavior. For example, a message tailored to their emotions might be created, such as, "You seem to be feeling a little stressed right now. If you need a change of pace, please reconsider your purchase."

[0483] This notification appears on the device, allowing users to re-evaluate their purchase in light of their own feelings. In addition, users can adjust the content and frequency of notifications, providing flexibility to meet individual needs.

[0484] Ultimately, this system aims to support wiser purchasing decisions by taking into account the dynamic shifts in emotions. This approach allows users to eliminate unconscious biases and achieve a more economically and emotionally satisfying purchasing experience.

[0485] The following describes the processing flow.

[0486] Step 1:

[0487] The device monitors the user's activity on e-commerce sites and collects shopping-related data. Specifically, it obtains data on searched keywords, viewed products, products added to the cart, and purchased products, and stores this as purchase history data.

[0488] Step 2:

[0489] The device's built-in emotion engine recognizes the user's emotional state in real time. Using the user's camera and microphone, it comprehensively evaluates the user's emotions through facial recognition and voice analysis. For example, it analyzes whether the user's face is smiling or their voice sounds tense.

[0490] Step 3:

[0491] The device sends collected purchase history data and sentiment data to the server. Data transmission is secure and encrypted to protect user privacy.

[0492] Step 4:

[0493] The server analyzes the received data based on behavioral economics models to identify the user's unconscious choice patterns. At the same time, it considers emotional data and evaluates how the current emotional state influences purchasing decisions.

[0494] Step 5:

[0495] The server generates the most suitable pop-up notification based on the analysis results. This notification is tailored to the user's emotional state and purchase history, and may include messages such as, "Let's consider if you really need this product. How about taking a different action to change your mood?"

[0496] Step 6:

[0497] The device displays the generated pop-up notification on the user's device screen. Through the displayed notification, the user can re-evaluate their purchasing behavior and make a more informed, emotion-based purchasing decision.

[0498] Step 7:

[0499] After receiving a pop-up notification, users decide whether to proceed with the purchase or reconsider. Furthermore, if the emotion engine's recognition accuracy or the notification content is inappropriate, users can adjust settings and customize the system.

[0500] (Example 2)

[0501] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0502] In users' shopping activities, unconscious choices and temporary emotional states can significantly influence purchasing behavior. This can lead to unnecessary purchases, potentially reducing users' economic and emotional satisfaction. To address this problem, there is a need for systems that enable users to make more informed purchasing decisions.

[0503] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0504] In this invention, the server includes means for monitoring the user's shopping activities and collecting purchase history data; means for acquiring emotional data and recognizing the user's emotional state; means for analyzing the purchase history data and emotional data based on behavioral economics models to identify the user's unconscious choices and emotional state; and means for displaying information related to the unconscious choices and emotional state as a pop-up notification on the user's information display device. This allows the user to recognize whether their choices are influenced by bias and to re-evaluate their purchasing behavior in consideration of their emotional state.

[0505] "User" is a term that refers to an individual who uses a computer or device to engage in shopping activities.

[0506] "Shopping activity" refers to the process by which consumers engage in purchase-related actions, such as searching for, browsing, and purchasing products online or offline.

[0507] "Purchase history data" refers to data that records and organizes information about products a user has searched for, viewed, and purchased in the past.

[0508] "Emotional data" refers to information that reflects the user's emotional state, obtained from factors such as facial expressions, tone of voice, and operation speed.

[0509] A "behavioral economics model" is a theoretical framework for analyzing the psychological and economic factors in users' purchasing behavior and predicting their actions and choices.

[0510] "Unconscious choices" refer to purchasing decisions and actions that users make without being aware of them.

[0511] "Emotional state" refers to the emotional condition or changes a user experiences at a particular moment.

[0512] A "pop-up notification" is an informational message displayed on a user's device screen, used to attract the user's attention and provide feedback on their actions.

[0513] This invention is a system that performs advanced analysis using purchase history data and sentiment data to better support users' purchasing behavior. This system includes a dedicated application installed on a terminal and a server that performs data analysis.

[0514] First, the device monitors the user's shopping activity and accumulates purchase history data in real time. This device is a computer device such as a smartphone or tablet, and has dedicated software installed. This software has the function of collecting data such as the URL of the web page, the time of visit, the product links clicked, and the items added to the cart.

[0515] In addition, the device is equipped with an emotion engine that recognizes the user's emotional state. This recognition is achieved through facial expression analysis using the camera, voice tone analysis using the microphone, and monitoring of operation speed using touch sensors. This allows the system to understand the fluctuations in the user's emotions while they are shopping.

[0516] The collected purchase history and sentiment data are sent to a server for analysis. The server comprehensively analyzes the received data using behavioral economics models to identify users' unconscious choices and emotional states. This analysis is performed particularly by modeling users' purchasing patterns and emotional changes, helping to better understand their intentions and needs regarding purchases.

[0517] Based on the analysis results, the server generates a pop-up notification tailored to the user's emotional state. This notification suggests that the user's choice may be biased and encourages them to re-evaluate their purchasing behavior. For example, a message like, "You seem to be feeling a little stressed right now. If you need a change of pace, please reconsider your purchase," might be possible.

[0518] Ultimately, the device provides these notifications to the user, and the user can adjust the content and frequency of these notifications. Furthermore, it's possible to improve the accuracy and effectiveness of notifications using generative AI models.

[0519] An example of a prompt is, "Analyze the emotional responses a user exhibits while online shopping and generate emotion-based purchasing advice."

[0520] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0521] Step 1:

[0522] The terminal monitors the user's shopping activity and collects purchase history data. Inputs include the user's search queries, visited product pages, and information about purchased items. A dedicated application installed on the terminal collects this data and stores it in a database. The output is detailed purchase history data of the user. This data reveals the user's preferences and interests and is used in the subsequent analysis step.

[0523] Step 2:

[0524] The device activates an emotion engine to recognize the user's emotional state. Inputs include facial expressions captured by the face camera, voice collected by the microphone, and motion data from the touch sensor. These input data are analyzed to obtain an output that evaluates the user's emotions. The analysis is performed using an emotion recognition algorithm to identify emotions such as whether the user is happy or stressed.

[0525] Step 3:

[0526] The device sends purchase history data and sentiment data to the server. The input is the purchase history data and sentiment data that were previously collected and recognized. This data is encrypted and sent to the server over the network. The output is the data that has safely reached the server and is ready to proceed to the next analysis stage.

[0527] Step 4:

[0528] The server analyzes the data it receives. Inputs include purchase history data and sentiment data. The server analyzes this data based on behavioral economics models to evaluate the user's unconscious choices and emotional states. The output is the analysis results, which are used in the next step. A generative AI model is used for the analysis to model the user's choice patterns and emotional influences.

[0529] Step 5:

[0530] The server generates a pop-up notification based on the analysis results. The input is the analyzed data on purchasing behavior and emotional state. Based on this, it generates a message that helps the user recognize that their choices were influenced by emotional bias. The output is a notification message delivered to the user, which encourages them to re-evaluate their purchase.

[0531] Step 6:

[0532] The device receives notifications from the server and displays them to the user. The input is the notification message sent from the server. The device receives this information and displays it on the user's screen. The output is a visually presented notification to the user, which they can then review and use to influence their purchasing behavior. The user can customize the content and frequency of these notifications as output conditions.

[0533] (Application Example 2)

[0534] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0535] Online shoppers often face the challenge of being influenced by unconscious biases and emotions, resulting in undesirable purchases. Furthermore, there is a lack of means to capture the impact of user emotions on purchasing behavior in real time and provide appropriate advice and information.

[0536] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0537] In this invention, the server includes means for monitoring the user's purchasing activities and collecting purchase history data, means for analyzing the purchase history data based on behavioral economics models to identify unintended choices, and means for analyzing emotional states in real time and integrating the analysis of that data. This enables appropriate purchasing support that takes into account the user's emotions.

[0538] A "user" refers to an individual or group that engages in online shopping, and their purchasing activities are monitored by the system.

[0539] "Purchase history data" refers to data that includes information such as terms a user has searched for in the past, product pages they have viewed, and items they have purchased.

[0540] A "behavioral economics model" is an economic theory that takes into account irrational human behavior, and is used to analyze purchasing behavior.

[0541] "Unintentional choices" refer to biased purchasing decisions made by users without their realizing it.

[0542] A "pop-up" is a notification or message that suddenly appears on the user's device screen.

[0543] "Emotional state" refers to the specific circumstances or conditions of a user's emotions, which the system recognizes through facial expressions and voice analysis.

[0544] "Integrated analysis methods" refer to methods that combine and analyze purchase history data and sentiment data, processing them as consistent information.

[0545] "Adjusting purchasing behavior" means that the system intervenes to support appropriate purchasing decisions based on the user's purchasing patterns and emotional state.

[0546] This invention is a system that supports users' purchasing behavior by monitoring their purchasing activities in real time, collecting purchase history data, and analyzing their emotional state. This system mainly consists of terminals and servers.

[0547] The device monitors the user's shopping activity. Specifically, it collects data in real time on the user's smartphone or computer regarding search terms, viewed product pages, and purchased items. This data is stored as purchase history data. Furthermore, the device has an emotion engine that analyzes the user's emotional state using multiple indicators, such as facial expressions, voice tone, and operation speed. The emotion data obtained from this analysis is sent to the server along with the purchase history data.

[0548] The server analyzes received purchase history data and sentiment data based on behavioral economics models. This analysis not only reveals the user's unconscious and potential biases but also evaluates their emotional state, providing information to suppress or encourage purchasing behaviors that users perform unconsciously. The server uses this information to generate appropriate pop-up notifications. These notifications are sensitive to the user's emotions and can, for example, create messages recommending products that match their current emotional state or encouraging them to re-evaluate their purchases.

[0549] The system provides precise and adaptive purchasing support based on emotions and purchase history by displaying pop-up notifications on the user's device screen. Users can adjust the content and frequency of notifications themselves, allowing them to have a purchasing experience tailored to their needs.

[0550] This format allows users to gain a deeper understanding of their own emotions and purchasing behavior, and overcome unconscious biases. Furthermore, the notifications generated by the system support less stressful and more emotionally satisfying purchasing decisions.

[0551] For example, if a user is experiencing everyday stress, the system may display a message such as, "Why not try some relaxation items?" If the user has previously purchased relaxing aromatherapy products, those products may be suggested again.

[0552] An example of a prompt for a generative AI model would be, "Consider the user's stressful situation and provide ideas for application notifications that promote relaxation."

[0553] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0554] Step 1:

[0555] The terminal monitors the user's purchasing activity. Specifically, the terminal collects data in real time regarding the user's search terms, viewed product pages, and purchased items. This data is recorded as purchase history data and used for subsequent processing steps.

[0556] Step 2:

[0557] The device analyzes the user's emotional state using an emotion engine. This involves using the smartphone's camera and microphone to capture the user's facial expressions, voice tone, and operation speed. The emotion engine uses this data as input to perform data processing and calculations to identify the user's emotions, and then outputs emotional data.

[0558] Step 3:

[0559] The terminal sends the collected purchase history data and sentiment data to the server. The input here is the data obtained in steps 1 and 2, and a data packet is generated as the output to be sent to the server.

[0560] Step 4:

[0561] The server analyzes the received purchase history data and sentiment data based on behavioral economics models. The data received in the previous step is used as input, and the data is processed integrally to output information about the user's unconscious choices and current emotional state.

[0562] Step 5:

[0563] The server generates a pop-up notification tailored to the user based on the analysis results. The server considers the target user's emotional state and purchasing tendencies, and uses a generation AI model with prompt text to create an appropriate message. The output is the message text.

[0564] Step 6:

[0565] The server sends the generated pop-up notification to the device. The output is received by the device as a notification and displayed on the user's device screen. Specifically, the user will see a message prompting them to re-evaluate the product before making a purchase.

[0566] Step 7:

[0567] The user re-evaluates the purchase based on the notification. In this step, the user checks the notification on their device and adjusts or postpones their purchase behavior according to the message.

[0568] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0569] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0570] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0571] [Fourth Embodiment]

[0572] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0573] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0574] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0575] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0576] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0577] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0578] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0579] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0580] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0581] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0582] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0583] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0584] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0585] This invention is a system aimed at reducing unconscious biased choices made by users when shopping on e-commerce sites. The specific operation of the system will be described below.

[0586] First, when a user uses an e-commerce site with their device, the device monitors the user's shopping activity and collects purchase history data. The collected data includes information about the keywords the user searched for, the products they viewed, the products they purchased, and the frequency and timing of those purchases.

[0587] Next, this data is periodically sent from the terminal to the server. Based on the received data, the server analyzes the user's purchasing behavior according to behavioral economics models. This analysis checks whether the user tends to repeatedly make similar choices under certain conditions and identifies unconscious choices.

[0588] If the analysis reveals unconscious choices made by the user, the server generates an appropriate warning message. This message is created using AI generation, with language optimized for the user's purchasing patterns and preferences. For example, a notification might say, "You have purchased similar items multiple times. Do you need them again?"

[0589] The generated notification is sent to the device and displayed as a pop-up on the user's device screen. This pop-up notification is intended to encourage conscious purchasing decisions and prevent future unnecessary purchases.

[0590] Furthermore, users can individually adjust the frequency and content of notifications. This allows for flexible system usage tailored to user preferences and needs, maximizing support for a personalized purchasing experience.

[0591] In this way, the system operates continuously in the background, appropriately supporting the user's purchasing behavior and possessing a practical form for promoting conscious and intelligent purchasing.

[0592] The following describes the processing flow.

[0593] Step 1:

[0594] The device monitors the user's shopping activity when they use an e-commerce site. During this time, it records keywords the user searches for, product pages viewed, purchased items, and the timing and frequency of these purchases. This forms the basis for collecting user purchase history data.

[0595] Step 2:

[0596] The device periodically sends collected purchase history data to the server. This transmission occurs when the user consents or when the system deems it appropriate. The transmitted data is encrypted and transferred in a privacy-protected manner.

[0597] Step 3:

[0598] The server analyzes the received purchase history data. This analysis applies behavioral economics models to identify users' unconscious choices and purchasing patterns from the data. Specifically, it determines whether users are over-selecting certain product categories or over-responding to discount campaigns.

[0599] Step 4:

[0600] The server generates a pop-up notification to warn the user based on the analysis results. A generation AI is used, and the notification is crafted with the most appropriate wording based on the user's purchasing behavior. For example, it might create a specific notification such as, "You've recently purchased many similar items. Please reconsider whether you need them now."

[0601] Step 5:

[0602] The device receives a pop-up notification sent from the server and displays it on the user's device screen. This notification appears in real time, giving the user an opportunity to pause and reconsider during the purchasing process. The notification is only displayed with the user's consent.

[0603] Step 6:

[0604] When users receive a pop-up notification, they review their purchasing behavior and make a decision. Depending on the content of the notification, they decide whether to cancel the purchase or reconsider and proceed. Users can also individually adjust their notification settings, allowing them to customize the frequency and content of notifications to their needs.

[0605] (Example 1)

[0606] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0607] In online shopping, users make purchasing decisions based on a variety of options, but under certain conditions, they may unconsciously make biased choices. This increases the risk of users purchasing unnecessary items, potentially leading to wasteful spending. Traditional systems do not adequately provide methods to identify such unconscious purchasing behavior and provide appropriate feedback to users.

[0608] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0609] In this invention, the server includes means for monitoring the user's purchasing behavior and collecting behavioral data, means for analyzing the behavioral data and identifying the user's unconscious choices, and means for generating warnings for the identified unconscious choices using a generative AI model. This makes it possible to analyze the user's purchasing behavior, automatically identify trends in unconscious choices, and provide feedback to the user.

[0610] "User" refers to an individual or organization that conducts online shopping through this system.

[0611] "Purchasing behavior" refers to the series of actions a user takes to search for, browse, select, and purchase a product.

[0612] "Behavioral data" refers to information related to a user's purchasing behavior, and includes data such as search keywords, product browsing history, purchase history, and purchase frequency.

[0613] "Unconscious choices" refer to biased product selections made unconsciously by users under specific conditions, and include choices that deviate from normal purchasing decisions.

[0614] A "generative AI model" refers to an artificial intelligence program that generates natural language based on a user's purchasing patterns, and its role is to generate messages optimized for the user.

[0615] A "warning" is a notification given to a user that includes messages intended to inform them of the existence of an unconscious choice and encourage conscious purchasing decisions.

[0616] "Notification" refers to information provided in the form of a warning displayed on the user's device screen, including pop-ups and other visual methods.

[0617] This invention is a system aimed at supporting users' purchasing behavior and reducing unconscious choices when using e-commerce platforms. The specific method for carrying out the invention is described below.

[0618] Users access online shopping sites using standard communication devices. These devices utilize specialized software to collect behavioral data such as user search history, product viewing history, and purchase history, in order to monitor user purchasing behavior. This software runs on the device and is capable of recording data in real time.

[0619] The collected behavioral data is transmitted to the server via a secure communication protocol. The server uses this data for detailed analysis. The analysis employs behavioral economics models and machine learning algorithms to perform calculations that identify whether users are making unconscious purchasing choices in specific patterns. Key technologies used by the server include database management systems and parallel processing techniques.

[0620] Based on the analysis results described above, the server uses a generative AI model to create a warning message for the user. This AI model generates prompts tailored to the user's past purchasing patterns, presenting the warning in an engaging and easy-to-understand manner. An example of a prompt is, "Create a message suggesting a new style to the user based on the fashion items they have purchased in the past."

[0621] The generated warning message is resent to the device and displayed as a pop-up notification on the user's device. This pop-up prompts the user to reconsider their current choice. Not only is this notification encouraged for more conscious purchasing decisions, but users can also freely adjust the notification settings. This provides the system with the flexibility to improve the user's individual purchasing experience.

[0622] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0623] Step 1:

[0624] The terminal uses dedicated software to collect behavioral data in real time when users shop online. Inputs include user search keywords, product browsing history, and purchase history. The terminal organizes this data and prepares it as structured data in a secure format. The output is the collected behavioral data for use in subsequent processing steps.

[0625] Step 2:

[0626] The device sends collected behavioral data to the server at regular intervals. The input is the structured data prepared in step 1. The device encrypts this data and sends it to the server using a secure communication protocol. The output is the behavioral data in a secure format sent to the server.

[0627] Step 3:

[0628] The server analyzes the received behavioral data. The input is behavioral data sent from the terminal. The server applies this data to a behavioral economics model to identify whether the user is unconsciously repeating similar purchasing choices. The output is the analysis results, including the user's unconscious choices.

[0629] Step 4:

[0630] The server prompts the generative AI model based on the analysis results to generate a warning message. The input is the analysis results obtained in step 3. The server utilizes the generative AI model to generate a natural language message optimized for the user's purchasing tendencies. The output is the generated warning message.

[0631] Step 5:

[0632] The server sends a generated warning message to the terminal, which then displays it as a pop-up on the user's device. The input is the warning message sent from the server. The terminal displays the notification in a visually clear format for the user. The output is the pop-up notification displayed to the user.

[0633] (Application Example 1)

[0634] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0635] On e-commerce platforms, users tend to unconsciously make biased purchasing choices, which can result in a lack of diversity in their purchasing behavior. Such bias can impair users' financial efficiency, narrow their product choices, and ultimately lead to decreased customer satisfaction. This invention aims to curb such biased purchasing choices and provide users with a more conscious and diverse purchasing experience.

[0636] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0637] In this invention, the server includes means for monitoring the user's purchasing activities and collecting purchase history data, means for analyzing the purchase history data based on a behavioral economics model to identify the user's unconscious choices, and means for displaying information related to the unconscious choices as a notification on the user's display device. This makes it possible to notify the user of biased purchasing tendencies in real time and suggest products from different categories.

[0638] A "user" is a consumer or individual who purchases goods or services using an e-commerce platform.

[0639] "Purchasing activity" refers to the series of actions a user takes on an e-commerce platform, from searching for products, adding them to their cart, placing an order, and making a purchase.

[0640] "Purchase history data" refers to data about products and services that a user has purchased in the past, including product name, purchase date, price, and quantity.

[0641] Behavioral economics models are statistical and economic analytical methods used to analyze user purchasing behavior and identify unconscious choices and biases.

[0642] "Unconscious choices" are purchasing decisions that users make without realizing it, based on past experiences and habits, and may involve specific patterns or biases.

[0643] A "notification" is an informational message displayed on the user's device screen, and may include feedback on the user's purchasing choices or purchase suggestions.

[0644] A "category" is a classification system based on the type and intended use of a product, serving as a criterion for users to compare and consider their options.

[0645] To implement this invention, the user must first access an e-commerce platform using a device such as a smartphone or smart glasses and begin shopping. The device has a mechanism to continuously collect the user's purchase history data and transmit it to a server at specific intervals. This collected data includes information about the products searched for, what was viewed, what was purchased, and the frequency and duration of these activities.

[0646] Upon receiving this data, the server performs an analysis using behavioral economics models. This analysis includes a process to check whether users are repeatedly making the same choices unconsciously. This reveals potential purchasing biases and inclinations.

[0647] If the server detects a biased purchasing pattern, it uses a generative AI model to generate a warning message for the user. This message is customized based on the user's past purchasing patterns and preferences. A typical message might be something like, "You've purchased multiple similar items. Why not explore a new category?"

[0648] This generated message is immediately sent to the device and displayed as a notification on the user's device screen. The user can use this notification to reconsider before making a purchase.

[0649] For example, if a user consistently purchases clothing from a specific brand, the server can suggest, "Try checking out collections from different designers." An example of a prompt would be, "Create advice suggesting new product categories based on the user's purchase history."

[0650] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0651] Step 1:

[0652] The device collects purchase history data when users engage in shopping activities on e-commerce platforms. Input includes the user's activity history, viewed items, purchased items, and their dates and times. The device aggregates this data and converts it into a data format for periodic transmission to the server. At this stage, the collected data is stored locally.

[0653] Step 2:

[0654] The server receives purchase history data sent from the terminal. This data is used as input and analyzed using a behavioral economics model. The analysis process uses clustering and pattern recognition techniques to check whether users are unconsciously making biased choices. The output generates identified biased choice patterns.

[0655] Step 3:

[0656] If the server detects biased selections as a result of its analysis, it uses a generative AI model to generate a warning message to notify the user. The inputs used are identified biased patterns and information about the user's past purchasing preferences. The output is a customized notification message in language that is easy for the user to understand.

[0657] Step 4:

[0658] The server sends the generated notification message to the device. The device displays the received message as a pop-up notification on the user's device screen. This gives the user an opportunity to reconsider their purchasing behavior.

[0659] Step 5:

[0660] Users can review their purchasing choices through notifications displayed on their devices. In this step, users reconsider whether or not to purchase based on the notification content. As an output, this promotes purchasing behavior in which conscious choices are made.

[0661] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0662] This invention provides more precise and adaptive purchasing support by adding an emotion engine for recognizing user emotions to a system that monitors user shopping activities and collects and analyzes purchase history data. The following describes a specific implementation of this system.

[0663] First, the device monitors the user's shopping activity in real time. During this process, it collects data on the terms the user searches for, the product pages they view, and the items they purchase, and stores this data as purchase history data.

[0664] Next, the emotion engine built into the device recognizes the user's emotions. Emotion recognition can be performed by combining multiple factors, such as the user's facial expression analysis, voice tone, and operation speed. The emotion data obtained here is sent to the server along with the purchase history data.

[0665] The server comprehensively analyzes received purchase history data and emotional data based on behavioral economics models. This analysis not only identifies the user's unconscious choices but also evaluates their emotional state, providing information that supports their purchase intentions and needs.

[0666] Once the analysis is complete, the server generates an optimal pop-up notification based on the user's purchase intent and emotional state. This notification takes the user's current emotions into consideration and is designed to encourage a reassessment of their purchasing behavior. For example, a message tailored to their emotions might be created, such as, "You seem to be feeling a little stressed right now. If you need a change of pace, please reconsider your purchase."

[0667] This notification appears on the device, allowing users to re-evaluate their purchase in light of their own feelings. In addition, users can adjust the content and frequency of notifications, providing flexibility to meet individual needs.

[0668] Ultimately, this system aims to support wiser purchasing decisions by taking into account the dynamic shifts in emotions. This approach allows users to eliminate unconscious biases and achieve a more economically and emotionally satisfying purchasing experience.

[0669] The following describes the processing flow.

[0670] Step 1:

[0671] The device monitors the user's activity on e-commerce sites and collects shopping-related data. Specifically, it obtains data on searched keywords, viewed products, products added to the cart, and purchased products, and stores this as purchase history data.

[0672] Step 2:

[0673] The device's built-in emotion engine recognizes the user's emotional state in real time. Using the user's camera and microphone, it comprehensively evaluates the user's emotions through facial recognition and voice analysis. For example, it analyzes whether the user's face is smiling or their voice sounds tense.

[0674] Step 3:

[0675] The device sends collected purchase history data and sentiment data to the server. Data transmission is secure and encrypted to protect user privacy.

[0676] Step 4:

[0677] The server analyzes the received data based on behavioral economics models to identify the user's unconscious choice patterns. At the same time, it considers emotional data and evaluates how the current emotional state influences purchasing decisions.

[0678] Step 5:

[0679] The server generates the most suitable pop-up notification based on the analysis results. This notification is tailored to the user's emotional state and purchase history, and may include messages such as, "Let's consider if you really need this product. How about taking a different action to change your mood?"

[0680] Step 6:

[0681] The device displays the generated pop-up notification on the user's device screen. Through the displayed notification, the user can re-evaluate their purchasing behavior and make a more informed, emotion-based purchasing decision.

[0682] Step 7:

[0683] After receiving a pop-up notification, users decide whether to proceed with the purchase or reconsider. Furthermore, if the emotion engine's recognition accuracy or the notification content is inappropriate, users can adjust settings and customize the system.

[0684] (Example 2)

[0685] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0686] In users' shopping activities, unconscious choices and temporary emotional states can significantly influence purchasing behavior. This can lead to unnecessary purchases, potentially reducing users' economic and emotional satisfaction. To address this problem, there is a need for systems that enable users to make more informed purchasing decisions.

[0687] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0688] In this invention, the server includes means for monitoring the user's shopping activities and collecting purchase history data; means for acquiring emotional data and recognizing the user's emotional state; means for analyzing the purchase history data and emotional data based on behavioral economics models to identify the user's unconscious choices and emotional state; and means for displaying information related to the unconscious choices and emotional state as a pop-up notification on the user's information display device. This allows the user to recognize whether their choices are influenced by bias and to re-evaluate their purchasing behavior in consideration of their emotional state.

[0689] "User" is a term that refers to an individual who uses a computer or device to engage in shopping activities.

[0690] "Shopping activity" refers to the process by which consumers engage in purchase-related actions, such as searching for, browsing, and purchasing products online or offline.

[0691] "Purchase history data" refers to data that records and organizes information about products a user has searched for, viewed, and purchased in the past.

[0692] "Emotional data" refers to information that reflects the user's emotional state, obtained from factors such as facial expressions, tone of voice, and operation speed.

[0693] A "behavioral economics model" is a theoretical framework for analyzing the psychological and economic factors in users' purchasing behavior and predicting their actions and choices.

[0694] "Unconscious choices" refer to purchasing decisions and actions that users make without being aware of them.

[0695] "Emotional state" refers to the emotional condition or changes a user experiences at a particular moment.

[0696] A "pop-up notification" is an informational message displayed on a user's device screen, used to attract the user's attention and provide feedback on their actions.

[0697] This invention is a system that performs advanced analysis using purchase history data and sentiment data to better support users' purchasing behavior. This system includes a dedicated application installed on a terminal and a server that performs data analysis.

[0698] First, the device monitors the user's shopping activity and accumulates purchase history data in real time. This device is a computer device such as a smartphone or tablet, and has dedicated software installed. This software has the function of collecting data such as the URL of the web page, the time of visit, the product links clicked, and the items added to the cart.

[0699] In addition, the device is equipped with an emotion engine that recognizes the user's emotional state. This recognition is achieved through facial expression analysis using the camera, voice tone analysis using the microphone, and monitoring of operation speed using touch sensors. This allows the system to understand the fluctuations in the user's emotions while they are shopping.

[0700] The collected purchase history and sentiment data are sent to a server for analysis. The server comprehensively analyzes the received data using behavioral economics models to identify users' unconscious choices and emotional states. This analysis is performed particularly by modeling users' purchasing patterns and emotional changes, helping to better understand their intentions and needs regarding purchases.

[0701] Based on the analysis results, the server generates a pop-up notification tailored to the user's emotional state. This notification suggests that the user's choice may be biased and encourages them to re-evaluate their purchasing behavior. For example, a message like, "You seem to be feeling a little stressed right now. If you need a change of pace, please reconsider your purchase," might be possible.

[0702] Ultimately, the device provides these notifications to the user, and the user can adjust the content and frequency of these notifications. Furthermore, it's possible to improve the accuracy and effectiveness of notifications using generative AI models.

[0703] An example of a prompt is, "Analyze the emotional responses a user exhibits while online shopping and generate emotion-based purchasing advice."

[0704] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0705] Step 1:

[0706] The terminal monitors the user's shopping activity and collects purchase history data. Inputs include the user's search queries, visited product pages, and information about purchased items. A dedicated application installed on the terminal collects this data and stores it in a database. The output is detailed purchase history data of the user. This data reveals the user's preferences and interests and is used in the subsequent analysis step.

[0707] Step 2:

[0708] The device activates an emotion engine to recognize the user's emotional state. Inputs include facial expressions captured by the face camera, voice collected by the microphone, and motion data from the touch sensor. These input data are analyzed to obtain an output that evaluates the user's emotions. The analysis is performed using an emotion recognition algorithm to identify emotions such as whether the user is happy or stressed.

[0709] Step 3:

[0710] The device sends purchase history data and sentiment data to the server. The input is the purchase history data and sentiment data that were previously collected and recognized. This data is encrypted and sent to the server over the network. The output is the data that has safely reached the server and is ready to proceed to the next analysis stage.

[0711] Step 4:

[0712] The server analyzes the data it receives. Inputs include purchase history data and sentiment data. The server analyzes this data based on behavioral economics models to evaluate the user's unconscious choices and emotional states. The output is the analysis results, which are used in the next step. A generative AI model is used for the analysis to model the user's choice patterns and emotional influences.

[0713] Step 5:

[0714] The server generates a pop-up notification based on the analysis results. The input is the analyzed data on purchasing behavior and emotional state. Based on this, it generates a message that helps the user recognize that their choices were influenced by emotional bias. The output is a notification message delivered to the user, which encourages them to re-evaluate their purchase.

[0715] Step 6:

[0716] The device receives notifications from the server and displays them to the user. The input is the notification message sent from the server. The device receives this information and displays it on the user's screen. The output is a visually presented notification to the user, which they can then review and use to influence their purchasing behavior. The user can customize the content and frequency of these notifications as output conditions.

[0717] (Application Example 2)

[0718] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0719] Online shoppers often face the challenge of being influenced by unconscious biases and emotions, resulting in undesirable purchases. Furthermore, there is a lack of means to capture the impact of user emotions on purchasing behavior in real time and provide appropriate advice and information.

[0720] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0721] In this invention, the server includes means for monitoring the user's purchasing activities and collecting purchase history data, means for analyzing the purchase history data based on behavioral economics models to identify unintended choices, and means for analyzing emotional states in real time and integrating the analysis of that data. This enables appropriate purchasing support that takes into account the user's emotions.

[0722] A "user" refers to an individual or group that engages in online shopping, and their purchasing activities are monitored by the system.

[0723] "Purchase history data" refers to data that includes information such as terms a user has searched for in the past, product pages they have viewed, and items they have purchased.

[0724] A "behavioral economics model" is an economic theory that takes into account irrational human behavior, and is used to analyze purchasing behavior.

[0725] "Unintentional choices" refer to biased purchasing decisions made by users without their realizing it.

[0726] A "pop-up" is a notification or message that suddenly appears on the user's device screen.

[0727] "Emotional state" refers to the specific circumstances or conditions of a user's emotions, which the system recognizes through facial expressions and voice analysis.

[0728] "Integrated analysis methods" refer to methods that combine and analyze purchase history data and sentiment data, processing them as consistent information.

[0729] "Adjusting purchasing behavior" means that the system intervenes to support appropriate purchasing decisions based on the user's purchasing patterns and emotional state.

[0730] This invention is a system that supports users' purchasing behavior by monitoring their purchasing activities in real time, collecting purchase history data, and analyzing their emotional state. This system mainly consists of terminals and servers.

[0731] The device monitors the user's shopping activity. Specifically, it collects data in real time on the user's smartphone or computer regarding search terms, viewed product pages, and purchased items. This data is stored as purchase history data. Furthermore, the device has an emotion engine that analyzes the user's emotional state using multiple indicators, such as facial expressions, voice tone, and operation speed. The emotion data obtained from this analysis is sent to the server along with the purchase history data.

[0732] The server analyzes received purchase history data and sentiment data based on behavioral economics models. This analysis not only reveals the user's unconscious and potential biases but also evaluates their emotional state, providing information to suppress or encourage purchasing behaviors that users perform unconsciously. The server uses this information to generate appropriate pop-up notifications. These notifications are sensitive to the user's emotions and can, for example, create messages recommending products that match their current emotional state or encouraging them to re-evaluate their purchases.

[0733] The system provides precise and adaptive purchasing support based on emotions and purchase history by displaying pop-up notifications on the user's device screen. Users can adjust the content and frequency of notifications themselves, allowing them to have a purchasing experience tailored to their needs.

[0734] This format allows users to gain a deeper understanding of their own emotions and purchasing behavior, and overcome unconscious biases. Furthermore, the notifications generated by the system support less stressful and more emotionally satisfying purchasing decisions.

[0735] For example, if a user is experiencing everyday stress, the system may display a message such as, "Why not try some relaxation items?" If the user has previously purchased relaxing aromatherapy products, those products may be suggested again.

[0736] An example of a prompt for a generative AI model would be, "Consider the user's stressful situation and provide ideas for application notifications that promote relaxation."

[0737] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0738] Step 1:

[0739] The terminal monitors the user's purchasing activity. Specifically, the terminal collects data in real time regarding the user's search terms, viewed product pages, and purchased items. This data is recorded as purchase history data and used for subsequent processing steps.

[0740] Step 2:

[0741] The device analyzes the user's emotional state using an emotion engine. This involves using the smartphone's camera and microphone to capture the user's facial expressions, voice tone, and operation speed. The emotion engine uses this data as input to perform data processing and calculations to identify the user's emotions, and then outputs emotional data.

[0742] Step 3:

[0743] The terminal sends the collected purchase history data and sentiment data to the server. The input here is the data obtained in steps 1 and 2, and a data packet is generated as the output to be sent to the server.

[0744] Step 4:

[0745] The server analyzes the received purchase history data and sentiment data based on behavioral economics models. The data received in the previous step is used as input, and the data is processed integrally to output information about the user's unconscious choices and current emotional state.

[0746] Step 5:

[0747] The server generates a pop-up notification tailored to the user based on the analysis results. The server considers the target user's emotional state and purchasing tendencies, and uses a generation AI model with prompt text to create an appropriate message. The output is the message text.

[0748] Step 6:

[0749] The server sends the generated pop-up notification to the device. The output is received by the device as a notification and displayed on the user's device screen. Specifically, the user will see a message prompting them to re-evaluate the product before making a purchase.

[0750] Step 7:

[0751] The user re-evaluates the purchase based on the notification. In this step, the user checks the notification on their device and adjusts or postpones their purchase behavior according to the message.

[0752] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0753] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0754] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0755] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0756] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0757] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0758] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0759] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0760] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0761] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0762] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0763] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0764] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0765] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0766] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0767] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0768] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0769] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0770] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0771] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0772] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0773] The following is further disclosed regarding the embodiments described above.

[0774] (Claim 1)

[0775] A means of monitoring users' shopping activities and collecting purchase history data,

[0776] A means for analyzing the aforementioned purchase history data based on a behavioral economics model to identify the user's unconscious choices,

[0777] A means for displaying information related to the aforementioned unconscious choice as a pop-up notification on the user's device screen,

[0778] A system that includes this.

[0779] (Claim 2)

[0780] The system according to claim 1, wherein the pop-up notification suggests that the user's choice is biased and encourages a re-evaluation of the purchasing behavior.

[0781] (Claim 3)

[0782] The system according to claim 1, wherein the frequency and content of the pop-up notification can be adjusted by the user.

[0783] "Example 1"

[0784] (Claim 1)

[0785] A means of monitoring user purchasing behavior and collecting behavioral data,

[0786] A means for analyzing the aforementioned behavioral data to identify the user's unconscious choices,

[0787] A means for generating warnings for identified unconscious choices using a generative AI model,

[0788] A means of displaying the generated warning as a notification on the user's information terminal,

[0789] A means to allow users to adjust the content and timing of notifications,

[0790] A system that includes this.

[0791] (Claim 2)

[0792] The system according to claim 1, wherein the notification suggests that the user's choice was made unconsciously and makes the user aware of the choice.

[0793] (Claim 3)

[0794] The system according to claim 1, wherein the notification content is personalized by a generating AI model.

[0795] "Application Example 1"

[0796] (Claim 1)

[0797] A device that monitors users' purchasing activities and collects purchase history data,

[0798] A device that analyzes the aforementioned purchase history data based on a behavioral economics model to identify the user's unconscious choices,

[0799] A device that displays information related to the aforementioned unconscious choice as a notification on the user's display device,

[0800] The aforementioned notification is provided by a device that analyzes the user's purchasing patterns in real time and recommends products from different categories when it recognizes a biased purchasing trend.

[0801] ...

[0802] A system that includes this.

[0803] (Claim 2)

[0804] The system according to claim 1, wherein the notification suggests that the user's choice is biased and encourages a re-evaluation of their purchasing behavior.

[0805] (Claim 3)

[0806] The system according to claim 1, wherein the frequency and content of the aforementioned notifications are adjustable by the user, and which recommends products from different categories based on a specific purchase pattern.

[0807] "Example 2 of combining an emotion engine"

[0808] (Claim 1)

[0809] A means of monitoring users' shopping activities and collecting purchase history data,

[0810] A means for acquiring the aforementioned emotional data and recognizing the user's emotional state,

[0811] A means for analyzing the aforementioned purchase history data and emotional data based on a behavioral economics model to identify the user's unconscious choices and emotional state,

[0812] A means for displaying information related to the aforementioned unconscious choices and emotional states as a pop-up notification on the user's information display device,

[0813] A system that includes this.

[0814] (Claim 2)

[0815] The system according to claim 1, wherein the pop-up notification suggests that the user's choice is biased and encourages a re-evaluation of the purchasing behavior with respect to the user's emotional state.

[0816] (Claim 3)

[0817] The system according to claim 1, wherein the frequency and content of the pop-up notification can be adjusted by the user.

[0818] "Application example 2 when combining with an emotional engine"

[0819] (Claim 1)

[0820] A means of monitoring users' purchasing activities and collecting purchase history data,

[0821] The aforementioned purchase history data is analyzed based on a behavioral economics model to identify unintended user choices,

[0822] A means of displaying information related to the aforementioned unintended selection as a pop-up on the user's device screen,

[0823] A means to analyze user emotions in real time and integrate that emotional data with purchase history data,

[0824] A means for adjusting the user's purchasing behavior based on the aforementioned emotional state and generating appropriate notifications,

[0825] A system that includes this.

[0826] (Claim 2)

[0827] The aforementioned pop-up notification suggests that the user's choice may be influenced by potential bias and encourages them to reconsider their purchasing behavior.

[0828] The system according to claim 1, wherein the notification generates a message tailored to the user's emotional state.

[0829] (Claim 3)

[0830] The system according to claim 1, wherein the frequency and content of the pop-up notifications can be adjusted by the user, and recommendations are provided according to the user's emotional state. [Explanation of symbols]

[0831] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of monitoring users' shopping activities and collecting purchase history data, A means for analyzing the aforementioned purchase history data based on a behavioral economics model to identify the user's unconscious choices, A means for displaying information related to the aforementioned unconscious choice as a pop-up notification on the user's device screen, A system that includes this.

2. The system according to claim 1, wherein the pop-up notification suggests that the user's choice is biased and encourages a re-evaluation of the purchasing behavior.

3. The system according to claim 1, wherein the frequency and content of the pop-up notification can be adjusted by the user.

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